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About This Role
About this role:
Wells Fargo is seeking a Principal Engineer in Cybersecurity as part of Identity and Access Management Engineering. Learn more about career areas and business divisions at wellsfargojobs.com
This role will serve as a senior technical leader responsible for defining the enterprise architecture and security strategy for agentic identity, including AI agents, autonomous systems, machine identities, workload identities, and other non-human access models.
This role will establish enterprise standards, reference architectures, security patterns, and governance controls that enable responsible AI innovation while maintaining strong security, accountability, auditability, and regulatory compliance.
This individual will partner across Identity and Access Management, Cybersecurity, Infrastructure, Product, Engineering, Data, AI Governance, Risk, Compliance, and Enterprise Architecture to deliver secure and scalable identity controls for AI agents and emerging autonomous technologies.
In this role, you will:
- Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups
- Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking
- Translate advanced technology experience, an in-depth knowledge of the organization’s tactical and strategic business objectives, the enterprise technological environment, the organizational structure, and strategic technological opportunities and requirements into technical engineering solutions
- Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions
- Maintain knowledge of industry best practices and emerging technologies and recommend innovations that enhance operations or provide a competitive advantage to the organization
- Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership
- Define the enterprise architecture and security strategy for AI agents, autonomous systems, machine identities, workload identities, and other non-human identities
- Establish reference architectures, engineering standards, reusable security patterns, solution blueprints, and guardrails for agentic identity initiatives
- Design authentication, authorization, delegation, lifecycle management, observability, accountability, and auditability models for AI agents
- Develop security patterns for agent registration, ownership, agent-to-agent communication, delegated access, capability-based authorization, and decommissioning
- Define identity-centric security controls that support Zero Trust principles and reduce reliance on standing privileges and long-lived credentials
- Architect integrations across enterprise Identity Governance and Administration, Privileged Access Management, cloud, directory, workload identity, and cybersecurity platforms
- Partner with product managers and engineering teams to translate strategic objectives into practical, scalable, and secure identity solutions
- Evaluate emerging platforms, frameworks, and vendor capabilities related to agentic identity, AI security, and non-human identity governance
- Ensure architecture and engineering solutions meet internal control, audit, risk, regulatory, privacy, and compliance requirements
- Demonstrate proficiency in using AI-assisted development and analysis tools, such as GitHub Copilot and approved code-centric agents
- Leverage AI to accelerate system design, coding, testing, analysis, and troubleshooting
- Apply strong technical judgment when validating and integrating AI-assisted outputs into solutions
- Understand and account for model limitations, security risks, and operational considerations
- Apply AI responsibly in development and production environments
- Ensure AI usage aligns with security, compliance, privacy, and ethical standards
Required Qualifications:
- 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 7+ years of experience in Identity and Access Management, cybersecurity, software engineering, or enterprise architecture
- 5+ years of experience designing enterprise identity architecture and identity-centric security solutions
Desired Qualifications:
- Experience defining enterprise architecture and security strategies for agentic AI, autonomous systems, machine identities, workload identities, or other non-human identities
- Experience securing AI agents, large language model integrations, agent orchestration frameworks, and autonomous platforms
- Experience designing authentication, authorization, delegated access, identity lifecycle management, observability, and accountability controls for AI agents
- Experience developing enterprise reference architectures, design standards, reusable security patterns, solution blueprints, and technical guardrails
- Deep understanding of Zero Trust architecture, identity-centric security models, least privilege, short-lived credentials, and policy-based authorization
- Experience designing enterprise-scale Identity Governance and Administration, Privileged Access Management, workload identity, and non-human identity solutions
- Experience with Microsoft Entra ID, Azure, Amazon Web Services, Google Cloud Platform, CyberArk, Saviynt, or similar enterprise identity and cloud platforms
- Strong knowledge of OAuth, OpenID Connect, SAML, SPIFFE/SPIRE, certificates, Public Key Infrastructure, API security, secrets management, and policy-based authorization
- Experience establishing governance controls for agent registration, ownership, delegated authority, monitoring, lifecycle management, and decommissioning
- Experience integrating identity platforms with cloud, container, API, automation, data, and AI ecosystems
- Experience establishing security controls that support auditability, traceability, and accountability for AI-driven actions
- Experience evaluating emerging technologies, platforms, frameworks, and vendor solutions related to AI security and identity
- Experience influencing engineering roadmaps, modernization initiatives, and enterprise-wide architecture decisions
- Experience leading large-scale identity modernization, platform transformation, or emerging technology initiatives
- Experience partnering with engineering, product, cybersecurity, infrastructure, data, AI governance, risk, compliance, and architecture teams
- Experience designing solutions for financial services or other highly regulated environments
- Ability to communicate complex identity and AI security concepts to engineers, executives, auditors, risk partners, and regulators
- Exceptional architecture, solution design, and technical documentation skills
- Proven ability to influence technical strategy and drive consensus across diverse stakeholder groups
- Relevant identity, cloud, architecture, or security certifications such as CISSP, CCSP, SABSA, TOGAF, or equivalent credentials
Locations:
- 300 S Brevard, Charlotte, North Carolina 28202
- 401 Las Colinas Blvd W Bldg. A, Irving, Texas 75039
- 3075 Loyalty Cir., Columbus, Ohio 43219
Posting Statements:
- Job posting may come down early due to volume of applicants.
- Required location(s) listed above. Relocation assistance is not available for this position.
- Salary range is determined by location of the job. May be considered for a discretionary bonus, Restricted Share Rights, or other long-term incentive awards.
- This position is not eligible for visa sponsorship.
Pay Range
Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities. $159,000.00 - $254,000.00Benefits
Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits - Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.
- Health benefits
- 401(k) Plan
- Paid time off
- Disability benefits
- Life insurance, critical illness insurance, and accident insurance
- Parental leave
- Critical caregiving leave
- Discounts and savings
- Commuter benefits
- Tuition reimbursement
- Scholarships for dependent children
- Adoption reimbursement
Posting End Date:
27 Sep 2026* Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
Wells Fargo Recruitment and Hiring Requirements:
a. Third-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
Salary Context
This $159K-$254K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wells Fargo, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $159K to $254K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Wells Fargo AI Hiring
Wells Fargo has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span Wilmington, CA, US, Charlotte, NC, US, San Francisco, CA, US. Compensation range: $254K - $300K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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